High-Throughput Flow Injection Analysis–Mass Spectrometry (FIA-MS) Fingerprinting for the Authentication of Tea Application to the Detection of Teas Adulterated with Chicory

Abstract

Tea is a broadly consumed beverage worldwide that is susceptible to fraudulent practices, including its adulteration with other plants such as chicory extracts. In the present work, a non-targeted high-throughput flow injection analysis-mass spectrometry (FIA-MS) fingerprinting methodology was employed to characterize and classify different varieties of tea (black, green, red, oolong, and white) and chicory extracts by principal component analysis (PCA) and partial least squares–discriminant analysis (PLS-DA). Detection and quantitation of frauds in black and green tea extracts adulterated with chicory were also evaluated as proofs of concept using partial least squares (PLS) regression. Overall, PLS-DA showed that FIA-MS fingerprints in both negative and positive ionization modes were excellent sample chemical descriptors to discriminate tea samples from chicory independently of the tea product variety as well as to classify and discriminate among some of the analyzed tea groups. The classification rate was 100% in all the paired cases—i.e., each tea product variety versus chicory—by PLS-DA calibration and prediction models showing their capability to assess tea authentication. The results obtained for chicory adulteration detection and quantitation using PLS were satisfactory in the two adulteration cases evaluated (green and black teas adulterated with chicory), with calibration, cross-validation, and prediction errors below 5.8%, 8.5%, and 16.4%, respectively. Thus, the non-targeted FIA-MS fingerprinting methodology demonstrated to be a high-throughput, cost-effective, simple, and reliable approach to assess tea authentication issues.

Targeted HPLC-UV Polyphenolic Profiling to Detect and Quantify Adulterated Tea Samples by Chemometrics

“…In previous publications, we evaluated the suitability of non-targeted fingerprinting strategies based on HPLC-UV and HPLC-FLD [ 22 ] and FIA-MS [ 23 ] for the characterization and classification of tea and chicory samples. Although acceptable results were obtained to discriminate tea varieties against chicory, no discrimination was accomplished between the different tea varieties under study.…”

“…Tea and chicory extracts were obtained following a previously established procedure [ 22 , 23 ]. Briefly, ca.…”

“…For example, non-targeted high-performance liquid chromatography with ultraviolet (HPLC-UV) and fluorescence (HPLC-FLD) detection followed by partial least squares-discriminant analysis (PLS-DA) were developed to classify and authenticate tea versus chicory [ 22 ]. Recently, we proposed a high-throughput non-targeted flow injection analysis-mass spectrometry (FIA-MS) methodology to classify and authenticate tea versus chicory [ 23 ]. Although, overall, both HPLC-UV-FLD and FIA-MS fingerprinting methodologies were able to discriminate chicory from any tea variety with acceptable classification rates, prediction errors for adulterated tea samples with chicory using partial least squares (PLS) regression were still very high for some tea varieties, especially for white and black teas [ 22 , 23 ].…”

“…Recently, we proposed a high-throughput non-targeted flow injection analysis-mass spectrometry (FIA-MS) methodology to classify and authenticate tea versus chicory [ 23 ]. Although, overall, both HPLC-UV-FLD and FIA-MS fingerprinting methodologies were able to discriminate chicory from any tea variety with acceptable classification rates, prediction errors for adulterated tea samples with chicory using partial least squares (PLS) regression were still very high for some tea varieties, especially for white and black teas [ 22 , 23 ].…”

EASY-FIA: A Readably Usable Standalone Tool for High-Resolution Mass Spectrometry Metabolomics Data Pre-Processing

“…FIA-HRMS has thus become a technique for untargeted metabolomics applications in clinical studies, providing analysis of many samples and fast screening time, as is the case for critically ill patients where patient stratification is time-sensitive [ 20 ]. FIA-HRMS has been also used in archaeological samples [ 21 ], to discriminate between different kinds of cannabinoids present in cannabis samples [ 22 ], or to characterize eventual food adulterations [ 23 ].…”